A GOLD MINE OF TEXT TEXT MINING VERSUS MANUAL CODING METHODS REGARDING QUALITY OF CARE IN LONG-TERM CARE FOR OLDER ADULTS

Abstract In long-term care for older adults, large amounts of text are collected that cover the quality of care, such as transcribed interviews, medical records, and text in electronic health records. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining (TM) could provide a solution, as this methodology can analyze large amounts of text automatically. Therefore, this study aims to compare TM to the gold standard of manual coding regarding sentiment analysis and thematic content analysis. Data was collected from interviews with residents (n=21), family (n=20) and care professionals (n=20). TM models were developed and compared to the manual approach. The results of the manual and TM approach were evaluated in three ways: accuracy, consistency and expert feedback. Accuracy showed how similar the approaches are. Consistency showed if an individual approach finds the same themes in similar text segments. Expert feedback was used to show the perceived correctness of the TM approach. The accuracy analysis showed that 81.8% of text segments receive the same sentiment code in both approaches and 83.7% of text segments receive the same thematic codes. Interviews coded by TM had a higher consistency compared to those coded manually. Expert feedback showed that the TM had a limited understanding of the context. However, it also showed certain inconsistencies of manual coding. The current study has shown that TM can be an effective tool for quickly and accurately identifying sentiment and thematic content in large amounts of textual data.

The COVID-19 pandemic has resulted in a worldwide increase in the number of adults who are socially isolated and experiencing high levels of loneliness.Although research has focused on social isolation (the lack of social contact) and loneliness (the perception of being alone), few longitudinal studies have examined isolation and loneliness together in the context of the COVID-19 pandemic.We introduce a novel index of resilience to loneliness (social asymmetry), which captures the degree of congruence or discordance between subjective feelings of loneliness and objective social contact (social isolation).Using seven longitudinal samples (Total N > 75,000) across three continents, we examined trajectories of social asymmetry across the onset of the pandemic using Bayesian multilevel discontinuous growth models.These allowed us to demonstrate differences in level and change of social asymmetry across the onset of the pandemic.Levels and change tended toward resilience over time but did not show significant changes across the onset of the pandemic.However, these results were not consistent across samples, indicating heterogeneity across countries, age groups, and more.We discuss how examining COVID-related changes in social asymmetry advances our understanding of (1) the extent to which broad societal challenges can induce short-and long-term impacts on trajectories of resilience and ( 2) what factors account for heterogeneity in adaptive responses to adversity.

TECHNOLOGY RESEARCH AND APPLICATIONS
Abstract citation ID: igad104.0265

A GOLD MINE OF TEXT TEXT MINING VERSUS MANUAL CODING METHODS REGARDING QUALITY OF CARE IN LONG-TERM CARE FOR OLDER ADULTS
Coen Hacking 1 , Hilde Verbeek 1 , Jan Hamers 2 , and Sil Aarts 1 , 1. Maastricht University,Maastricht,Limburg,Netherlands,2. CAPHRI,Maastricht University,Maastricht,Limburg,Netherlands In long-term care for older adults, large amounts of text are collected that cover the quality of care, such as transcribed interviews, medical records, and text in electronic health records.Researchers currently analyze textual data manually to gain insights, which is a time-consuming process.Text mining (TM) could provide a solution, as this methodology can analyze large amounts of text automatically.Therefore, this study aims to compare TM to the gold standard of manual coding regarding sentiment analysis and thematic content analysis.Data was collected from interviews with residents (n=21), family (n=20) and care professionals (n=20).TM models were developed and compared to the manual approach.The results of the manual and TM approach were evaluated in three ways: accuracy, consistency and expert feedback.Accuracy showed how similar the approaches are.Consistency showed if an individual approach finds the same themes in similar text segments.Expert feedback was used to show the perceived correctness of the TM approach.The accuracy analysis showed that 81.8% of text segments receive the same sentiment code in both approaches and 83.7% of text segments receive the same thematic codes.Interviews coded by TM had a higher consistency compared to those coded manually.Expert feedback showed that the TM had a limited understanding of the context.However, it also showed certain inconsistencies of manual coding.The current study has shown that TM can be an effective tool for quickly and accurately identifying sentiment and thematic content in large amounts of textual data.Persons with mild cognitive impairment (PwMCI) are at risk of medication nonadherence due to prospective memory deficits.Over time, they are likely to experience accelerated deterioration in cognitive functions and may develop dementia when comorbid treatable health conditions, such as hypertension, are not well managed.A digital therapeutic system called Medication Education, Decision Support, Reminding, and Monitoring (MEDSReM©) has been designed for cognitively normal older adults to support hypertension medication adherence and is currently being tested in a randomized controlled trial.MEDSReM has the potential to support PwMCI with hypertension medication adherence, but it must be optimized specifically for this population.We conducted a cognitive walkthrough to identify usability issues and inform the redesign and optimization of the system for PwMCI.This usability technique is used to evaluate a system from a user's perspective and is typically conducted by subject matter experts.We had 12 individuals with interdisciplinary expertise in cognitive aging, cognitive impairment, hypertension management, nursing, human factors, and health technology participate in the cognitive walkthrough.Usability issues related to figure-ground contrast, text complexity, and flexibility related to decision making were identified.Overall, recommendations included (1) simplifying the user interface (e.g., color contrast/textual cues), (2) eliminating options related to medication taking actions (e.g., snooze, take later) to mitigate memory-related errors, and (3) providing more direct, prescriptive instructions to minimize decision-making demands.Based on these recommendations, MEDSReM is currently being redesigned for testing with PwMCI.Our approach can inform the design of other mobile health tools.

Moon Choi, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
The growing number of older adults, particularly those living alone, demands innovative health and social care solutions for the aging population.Mobile apps and other technologies are emerging every year to enhance the efficiency and effectiveness of geriatric health professionals' work management.However, limited knowledge exists regarding geriatric health professionals' perceptions of technology.The acceptance of technology is critical because emerging technologies cannot be fully utilized in the field without a positive attitude towards accepting them.The current study aims to investigate geriatric health professionals' perceptions and attitudes towards technology and artificial intelligence (AI).The data were collected from a national survey of health professionals specialized in home visits conducted by community health centers in South Korea (N=1,026).Technology acceptance was evaluated using the Technology Acceptance Model Questionnaire, and study participants were asked about their self-reported knowledge of AI.A series of regression models showed that younger age, higher education level, and male gender were positively associated with higher technology acceptance levels.Regarding AI knowledge, only younger age was positively associated with better AI knowledge.The findings suggest the existence of a cohort difference in perceptions and attitudes towards technology among geriatric health professionals, which warrants further investigation into the underlying mechanism.

SOCIAL MEDIA USE AND MENTAL HEALTH AMONG OLDER ADULTS WITH COMORBIDITIES
Long Chen 1 , Zuoting Nie 1 , Yun Jiang 2 , and Rumei Yang 1 , 1. Nanjing Medical University School of Nursing, Nanjing, Jiangsu, China (People's Republic), 2. University of Michigan, Ann Arbor, Michigan, United States Psychological distress is common and problematic for older adults with comorbidities.Social media has great potential to help older adults engage in meaningful social activities and relationships that might improve mental health.However, little information exists about the actual use and the beneficial effects of the use of social media on mental health among this particular population.Using data from the 2019-2020 Health Information National Trends Survey (HINTS), we aimed to describe the prevalence of social media use, and whether social media use is related to better self-care confidence and thus related to better mental health among U.S. older adults with comorbidities.Data were analyzed using weighted logistic models and mediation analyses that considered the complex survey design with jackknife replications.Variables were assessed by self-reports.The prevalence of social media use increased rapidly from 41% in 2017 to 54% in 2020 among U.S. older adults with comorbidities.Those who were of younger age, women, higher education, or higher income were more likely to use social media, whereas those who were Hispanic or Black were less likely to use social media.Social media use was related to better self-care confidence (b=0.17,95% CI=0.07-0.26,P< 0.001) and is further related to better mental health (b=1.07,95% CI=0.86-1.28,P< 0.001).Our results suggest that social media is a beneficial supplement to traditional sources of